Machine learning-based prediction model for the yield of nitrogen-enriched biomass pyrolysis products: Performance evaluation and interpretability analysis

• Published in: Journal of analytical and applied pyrolysis 2024-09, Vol.182, p.106723, Article 106723
• Main Authors: Bi, Dongmei, Wang, Hui, Liu, Yinjiao, Qin, Zhaojie, Song, Xiaoyv, Liu, Shanjian
• Format: Article
• Language: English
• Subjects: Feature selection; Machine learning; Nitrogen-rich biomass; Process optimization; Pyrolysis technology
• ISSN: 0165-2370
• DOI: 10.1016/j.jaap.2024.106723

The process optimization and control of nitrogen-rich biomass pyrolysis technology is crucial. This technology effectively converts agricultural waste into high-value energy products. The aim of this study is to develop an accurate model for predicting the yield of nitrogen-rich biomass pyrolysis products using machine learning techniques. The goal is to improve both the yield and quality of the products. This study uses machine learning techniques to develop an accurate model for predicting the yield of three-phase products from nitrogen-rich biomass pyrolysis. The goal is to optimize the biomass energy conversion process. The study builds a dataset containing 468 samples. This dataset comprehensively characterizes the chemical, elemental, and industrial properties of biomass, as well as the pyrolysis operating parameters. Models with high prediction accuracy are achieved by comparing various machine learning algorithms. These algorithms include Random Forest, GBDT, SVR, and ANN. Fine feature selection and hyper-parameter tuning are used to enhance the performance. In particular, the optimized GBDT model achieves a performance of MSE of 10.04 and R² of 0.92 in cross-validation. SHAP value analysis is used to further interpret the prediction results of the model. Sensitivity analysis, conducted through Monte Carlo simulation, identifies key features that have a high impact on the target value. Additionally, two-feature partial correlation analysis reveals the importance and dependence of the model features. This provides crucial technical support and a scientific basis for the industrial application of biomass energy and the optimization of the pyrolysis process. •Built a comprehensive dataset of 468 samples.•A variety of machine learning algorithms are compared and optimized.•SHAP value analysis enhances the explanatory power of key features of the model.•Monte Carlo sensitivity analysis of the sensitivity of key features.•Dual feature analysis reveals complex relationships among key features.